Analysis of a fixed center effect additive rates model for recurrent event data

He, Haijin; Pan, Deng; Sun, Liuquan; Li, Yimei; Robison, Leslie L.; Song, Xinyuan

doi:10.1016/j.csda.2017.03.003

Cited by 6 publications

(5 citation statements)

References 14 publications

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“…Next, we will prove the asymptotic normality. For the asymptotic distribution of̂0(t,̂), follow the same argument as shown in the work He et al, 16 n 1∕2…”

Section: Proof Of Theoremmentioning

confidence: 83%

“…15 When absolute rate differences are of interest, an alternative model called the additive rates model, which assumes that the effects of covariates are summarized by rate differences, can be employed. The additive rates model may be preferred due to its easy interpretation in practice because the absolute effect on the rate function can be determined from a regression coefficient 14,16 and provides a more adequate fit in many situations. 17 For data with intermittent gaps, the underlying mean function and regression parameters are estimated for the multiplicative model.…”

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confidence: 99%

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Semiparametric additive rates model for recurrent events data with intermittent gaps

Zhong

2018

Statistics in Medicine

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Statistical methods for analyzing recurrent events have attracted significant attention. The majority of existing works consider situations in which subjects are observed over time periods and events of interest that occurred during the course of follow-up are recorded. In some applications, a subject may leave the study for a period of time and then resume due to various reasons. During the absence, which is referred to as an intermittent gap in this study, it may be impossible to observe a recording of the event. A naive analysis disregards gaps and considers events to be a typical recurrent event dataset. However, this may result in biased estimations and misleading results. In this study, we build an additive rates model for recurrent event data considering intermittent gaps. We provide the asymptotic theories behind the proposed model, as well as the goodness of fit between observed and modeled values. Simulation studies reveal that the estimations perform well if intermittent gaps are taken into account. In addition, we utilized the longitudinal cohort of elderly patients who have type 2 diabetes and at least one record of a severe recurrent complication, hypoglycemia, from the National Health Insurance Research Database in Taiwan to demonstrate the proposed method.

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“…Next, we will prove the asymptotic normality. For the asymptotic distribution of̂0(t,̂), follow the same argument as shown in the work He et al, 16 n 1∕2…”

Section: Proof Of Theoremmentioning

confidence: 83%

mentioning

confidence: 99%

Semiparametric additive rates model for recurrent events data with intermittent gaps

Zhong

2018

Statistics in Medicine

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“…We can cite Fosen et al [26], Zhao et al [27], Giorgio et al [28], Novák [29], Li et al [30], where ABAO maintenances with static covariates are studied and estimation procedures are given and evaluated. Other designs were given by He et al [31], Maitra et al [32], Ning et al [33] and Xu et al [34]. Recurrent events data along with terminal event and covariates are discussed by Andersen et al [35], Han et al [36], Han et al [37], Kim [38] and Sun et al [39].…”

Section: Contextmentioning

confidence: 99%

Virtual age models with time-dependent covariates: A framework for simulation, parametric inference and quality of estimation

Brenière

Doyen

Bérenguer

2020

Reliability Engineering & System Safety

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A repairable system faces some failures and imperfect maintenances throughout its lifetime. If several identical and independent systems are considered together, some differences may arise between the systems, such as the geographical location or the maintenance team for example, which are constant information, or the weather conditions, which vary with time. This observed heterogeneity will influence more or less the failure process. In this paper, we include these data in a generalized virtual age model with the use of covariates. Then we estimate simultaneously the effect of the maintenances, that of the covariates, and the intrinsic wear of the systems. We also propose two simulation methods as well as a numerical estimation procedure. Then we assess the quality of the estimation of the parameters with a thorough simulation study.

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“…For unmixed recurrent-event data with clusters, Cai (2005a, 2005b) extended the semiparametric proportional rates models for recurrent-event data proposed by Lin et al (2000) to account for within-cluster correlation, with common or cluster-specific baseline rates. Liu et al (2012Liu et al ( , 2014 additionally discussed the estimation of cluster effects based on Schaubel and Cai (2005b) with proportional mean/rate model, while He et al (2017) proposed to estimate the cluster effects with additive rates models.…”

Section: Introductionmentioning

confidence: 99%

Statistical analysis of clustered mixed recurrent-event data with application to a cancer survivor study

Zhu

Choi

et al. 2020

Lifetime Data Anal

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In long-term follow-up studies on recurrent events, the observation patterns may not be consistent over time. During some observation periods, subjects may be monitored continuously so that each event occurence time is known. While during the other observation periods, subjects may be monitored discretely so that only the number of events in each period is known. This results in mixed recurrent-event and panel-count data. In these data, there is dependence among withinsubject events. Furthermore, if the data are collected from multiple centers, then there is another level of dependence among within-center subjects. Literature exists for clustered recurrent-event data, but not for clustered mixed recurrent-event and panel-count data. Ignoring the cluster effect may lead to less efficient analysis. In this paper, we present a marginal modeling approach to take into account the cluster effect and provide asymptotic distributions of the resulting regression parameters. Our simulation study demonstrates that this approach works well for practical situations. It was applied to a study comparing the hospitalization rates between childhood cancer survivors and healthy controls, with data collected from 26 medical institutions across North America during more than 20 years of follow-up.

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Analysis of a fixed center effect additive rates model for recurrent event data

Cited by 6 publications

References 14 publications

Semiparametric additive rates model for recurrent events data with intermittent gaps

Semiparametric additive rates model for recurrent events data with intermittent gaps

Virtual age models with time-dependent covariates: A framework for simulation, parametric inference and quality of estimation

Statistical analysis of clustered mixed recurrent-event data with application to a cancer survivor study

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